Cancer Is The Second Largest Deadliest Disease Biology Essay

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The human race is blessed with all short of things from the environment they are living to the technologies they created. But not all blessings blessed them, some of them changed into curse because of human activities. One such change is due to the adopting easy way of living. This human mentality not only create mental illness but also affect physical health this human life style bring out many deadly diseases and increase the existing diseases to the vast in number for example cancer.

Cancer is the second largest deadly diseases that spoil the human race with increasing mortality rate every year. All human parts are prone to this disease. The most commonly affected parts includes brain, lugs and breast among which breast cancer is not at all taken as serious matter over a past decade but a welcoming news is that that social awareness of this increases among the people in recent past.

Breast cancer, how worst is the disease can't express but a report says that every one among 22 women in India are affected by this disease and this rate tends to increase, and India ranked second largest next to America in this disease.

The experts tells us the reason for its wide spread, the first is that due to the stress, food habits and second is that it take much time to detect the early stage itself due to manual transferring of information. The first part is on individual hand but when on considering second we can do something. Thus an effective tool is needed to transfer this file immediately as soon as they capture for diagnosis. On the other hand telemetry provides platform to measure the parameters in distant. Now days, bio telemetry is widely used for measuring and monitoring the single dimension parameters such as EEG, ECG, temperature and pressure etc. It also used to study some other species anatomy. Thus complex medical diagnosis of breast cancer process carried in a less time using 2D-bio-telemetry. Besides static two-dimensional images, the field also covers the processing of time-varying signals such as video and the output of tomography equipment. Some techniques, such as morphological image processing, are specific to binary or grayscale images.

1.1 Applications

Photography and printing.

Satellite image processing.

Machine Vision.

Medical image processing.

Face detection, feature detection, face identification.

Microscope image processing.


X-ray slice data is generated using an X-ray source that rotates around the object; X-ray sensors are positioned on the opposite side of the circle from the X-ray source. The earliest sensors were scintillation detectors, with photomultiplier tubes excited by (typically) cesium iodide crystals. Cesium iodide was replaced during the eighties by ion chambers containing high pressure Xenon gas. These systems were in turn replaced by scintillation systems based on photo diodes instead of photomultipliers and modern scintillation materials with more desirable characteristics. Many data scans are progressively taken as the object is gradually passed through the gantry. They are combined together by the mathematical procedures known as topographic reconstruction. The data are arranged in a matrix in memory, and each data point is convolved with its neighbors according with a seed algorithm using Fast Fourier Transform techniques. This dramatically increases the resolution of each Volex (volume element). Then a process known as Back Projection essentially reverses the acquisition geometry and stores the result in another memory array. This data can then be displayed, photographed, or used as input for further processing, such as multi-planar reconstruction.

Newer machines with faster computer systems and newer software strategies can process not only individual cross sections but continuously changing cross sections as the gantry, with the object to be imaged, is slowly and smoothly slid through the X-ray circle. These are called helical or spiral CT machines. Their computer systems integrate the data of the moving individual slices to generate three dimensional volumetric information (3D-CT scan), in turn viewable from multiple different perspectives on attached CT workstation monitors. This type of data acquisition requires enormous processing power, as the data are arriving in a continuous stream and must be processed in real-time.


During the procedure, the breast is compressed by a dedicated mammography machine to even out the tissue, to increase image quality, and to hold the breast still (preventing motion blur). Both front and side images of the breast are taken. Deodorant, talcum powder or lotion may show up on the X-ray as calcium spots, and women are discouraged from applying these on the day of their investigation.

Until some years ago, mammography was typically performed with screen-film cassettes. Now, mammography is undergoing transition to digital detectors, known as Full Field Digital Mammography (FFDM). This progress is some years later than in general radiology.

This is due to several factors:

the higher resolution demands in mammography,

significantly increased expense of the equipment,

The fact that digital mammography has never been shown to be superior to film-screen mammography for the diagnosis of breast cancer.

Computed radiography (CR) may help speed the transition. CR allows facilities to continue to use their existing screen-film units but do the cassettes with an imaging plate that acts as a digital adapter.

3.1 Work Up Process

In the past several years, the "work-up" process has become quite formalized. It generally consists of screening mammography, diagnostic mammography, and biopsy when necessary, often performed via stereotactic core biopsy or ultrasound-guided core biopsy. After a screening mammogram, some women may have areas of concern which can't be resolved with only the information available from the screening mammogram. They would then be called back for a "diagnostic mammogram". This phrase essentially means a problem-solving mammogram. During this session, the radiologist will be monitoring each of the additional films as they are taken by a technologist. Depending on the nature of the finding, ultrasound may often used at this point, as well.

3.2 Outcome

Often women are quite distressed to be called back for a diagnostic mammogram. Most of these recalls will be false positive results. About 10 of these will be referred for a biopsy; the remaining 60 are found to be of benign cause. Of the 10 referred for biopsy, about 3.5 will have a cancer and 6.5 will not. Of the 3.5 who do have cancer, about 2 have a low stage cancer that will be essentially cured after treatment. Mammogram results are often expressed in terms of the BI-RADS Assessment Category, often called a "BI-RADS score." The categories range from 0 (Incomplete) to 6 (Known biopsy - proven malignancy).

While mammography is the only breast cancer screening method that has been shown to save lives, it is not perfect. Estimates of the numbers of cancers missed by mammography are usually around 10%-30%. This means that of the 350 per 100,000 women who have breast cancer, about 35-70 will not be seen by mammography. Reasons for not seeing the cancer include observer error, but more frequently it is because the cancer is hidden by other dense tissue in the breast and even after retrospective review of the mammogram, cannot be seen. Furthermore, one form of breast cancer, lobular cancer, has a growth pattern that produces shadows on the mammogram which are indistinguishable from normal breast tissue.

3.3 Benefits

Improved contrast between dense and non-dense breast tissue

Faster image acquisition (less than a minute)

Shorter exam time (approximately half that of film-based mammography)

Easier image storage

Physician manipulation of breast images for more accurate detection of breast cancer

Ability to correct under or over-exposure of films without having to repeat mammograms

Transmittal of images over phone lines or a network for remote consultation with other physicians


It is used for communication purpose to transmit and receive messages. ZigBee technology is a low data rate, low power consumption, low cost; wireless networking protocol targeted towards automation and remote control applications.

IEEE 802.15.4 committee started working on a low data rate standard a short while later. Then the ZigBee Alliance and the IEEE decided to join forces and ZigBee is the commercial name for this technology.

The name ZigBee is said to come from the domestic honeybee which uses a zigzag type of dance to communicate important information to other hive members. This communication dance (the "ZigBee Principle") is what engineers are trying to emulate with this protocol a bunch of separate and simple organisms that join together to tackle complex tasks.

4.1 Key Features XBee-PRO

2.4 GHz for worldwide deployment

Multipoint network topologies

900 MHz for long-range deployment

Multiple antenna options.

Low power and long range variants available.

4.2 For Long Range Integrity

Indoor/Urban: up to 300' (90 m), 200' (60m) for International variant

Outdoor line-of-sight: up to 1 mile (1600m), 2500' (750 m) for International variant

Transmit Power: 63mW (18dBm), 10mW (10dBm) for International variant

Receiver Sensitivity: -100 dBm

RF Data Rate: 250,000 bps

4.3 Advance Networking and Security

Retries and Acknowledgements

DSSS (Direct Sequence Spread Spectrum)

Each direct sequence channels has over 65,000 unique network addresses available

Source/Destination Addressing

Uncast & Broadcast Communications

Point-to-point, point-to-multipoint and peer-to-peer topologies supported.

4.4 Advantages

No configuration necessary for out-of box RF communications

AT and API Command Modes for configuring module parameters

Extensive command set

Small form factor

It does not need high data rate.

It is compatible.

It consumes less power.


Successful treatment of breast cancer depends on early detection and diagnosis of breast abnormalities and lesions. Mammography is the best available examination for the detection of early signs of breast cancer such as masses, calcifications, bilateral asymmetry and architectural distortion. Because of the limitations of human observers, computers have major role in detecting early signs of cancer. Wide range of features that define abnormalities and the fact that they are often indistinguishable from the surrounding tissue makes the computer-aided detection and diagnosis of breast abnormalities a challenge. This chapter discusses breast lesions and their features also this chapter briefly presents some of the developed computer-aided detection and diagnosis methods for each lesion.


The ACR (American College of Radiology) Breast Imaging Reporting and Data System (BI-RADSĀ®) suggest a standardized method for breast imaging reporting. Terms have been developed to describe breast density, lesion features and lesion classification. Depending on the amount of fibro glandular tissue, breast tissue seen on mammogram can be divided into four categories.

The breast is almost entirely fat when there is less than 25% fibro glandular tissue. Scattered fibro glandular dense breast tissue has between 25% And 50% fibro glandular tissue and heterogeneously dense breast tissue has between 51% and 75% fibro glandular tissue. When the breast is consisting of more than 75% fibro glandular tissue the breast is extremely dense. In the latter case sensitivity of mammography exam is decreased and the diagnosis of malignant lesions is more difficult.

Many lesions (masses, calcifications, architectural distortion and bilateral asymmetry) are defined with wide range of features. The features determine lesions shape, size, distribution, margins etc. Some of the lesions can be easily overlooked because of the poor feature visibility. One of the problems that appear in diagnosis of malignant lesions is incorrect classification of Lesions. Final assessment and classification of mammograms is made using ACR BI-RADS categories. A negative diagnostic examination is one that is negative, with a benign or probably benign finding (BI-RADS 1, 2 or 3) and a positive Diagnostic examination is one that requires a tissue diagnosis (BI-RADS 4 or 5) or the one with biopsy proof of malignancy (BI-RADS 6). If the finding can

Not be assessed, an additional imaging evaluation and/or prior mammograms are needed for comparison (BI-RADS 0).

fig : Examples of mammograms, each of different category of breast tissue: (a) fat breast tissue, (b) scattered fibro glandular dense breast tissue, (c) heterogeneously dense breast tissue and (d) extremely dense breast tissue

5.2 MASS

A mass is defined as a space occupying lesion seen in at least two different projections. If a potential mass is seen in only a single projection it should be called 'Asymmetry' or 'Asymmetric Density' until its three-dimensionality is confirmed. Masses have different density (fat containing, low density, is dense, high density), different margins (circumscribed, micro lobular, obscured, indistinct, speculated) and different shape (round, oval, lobular, irregular).

Fat-containing radiolucent and mixed-density circumscribed lesions are benign, whereas is dense to high-density masses may be of benign or malignant origin. Benign lesions tend to be is dense or of low density, with very well defined margins and surrounded by a fatty halo, but this is certainly not diagnostic of benignancy. The halo sign is a fine radiolucent line that surrounds circumscribed masses and is highly predictive that the mass is benign.

Circumscribed (well-defined or sharply-defined) margins are sharply demarcated with an abrupt transition between the lesion and the surrounding tissue. Without additional modifiers there is nothing to suggest infiltration. A mass with circumscribed margin is shown in Lesions with micro lobular margins have wavy contours. Obscured (erased) margins of the mass are erased because of the superimposition with surrounding tissue. This term is used when the physician is convinced that the mass is sharply-defined but has hidden margins. The poor definition of indistinct (ill defined) margins raises concern that there may be infiltration by the lesion and this is not likely due to superimposed normal breast tissue. The lesions with speculated margins are characterized by lines radiating from the margins of a mass.

A lesion that is ill-defined or speculated and in which there is no clear history of trauma to suggest hematoma or fat necrosis suggests a malignant process Shape of a mass can characterize it as benign or malignant. Masses with irregular shape usually indicate malignancy regularly shaped masses such as round and oval very often indicate a benign change.

Examples of (a) circumscribed mass and (b) speculated mass


As it is already said, a typical benign mass has a round, smooth and well circumscribed boundary. On the other hand, a malignant tumor usually has a speculated, rough and blurry boundary. However, there exist atypical cases of macrolobulated or speculated benign masses, as well as microlobulated or well-circumscribed malignant tumors. The detection of masses requires the segmentation of all possible suspicious regions, which may then be subjected to a series of tests to eliminate false positives.

Masses can have a range of sizes. Cancerous lesions are stochastic biologic phenomena that manifest in images as having various structures occurring at different sizes and over ranges of spatial scales. The boundaries of masses require a localized approach, although the sharpness and hence the scales of interpretation of the lesion boundaries, can vary considerably.

Moreover, the Speculations that are associated with many cancerous lesions occur with different widths, lengths and densities, which suggest that their characterization will require analysis over scales.

Some of the researchers have used texture features to discriminate between mass and normal tissue. Others have defined a number of features that were designed to capture image characteristics like intensity, is-density, location and contrast. Most diagnosis algorithms (CADx) begin with a region of interest (ROI) containing a suspicious mass. In the preprocessing step, the mass is segmented from the background normal tissue. Then the features that capture the difference between malignant and benign masses are extracted. Most features are designed to capture the shape and margin characteristics of masses. These features can be organized into morphologic features and texture features. Finally, masses are classified as malignant or benign. Some researchers have also proposed classification of masses into other categories, such as round, nodular or stellate, or such as fibro adenoma, cyst, or cancer.


Calcifications are tiny granule like deposits of calcium and are relatively bright (dense) in comparison with the surrounding normal tissue. Calcifications detected on mammogram are important indicator for malignant breast disease.

Unfortunately, calcifications are also present in many benign changes. Malignant calcifications tend to be numerous, clustered, small, varying in size and shape, angular, irregularly shaped and branching in orientation. Benign calcifications are usually larger than calcifications associated with malignancy. They are usually coarser, often round with smooth margins, smaller in number, more diffusely distributed, more homogeneous in size and shape and are much more easily seen on a mammogram. One of the key differences between benign and malignant calcifications is the roughness of their shape.

Typically benign calcifications are skin calcifications, vascular calcifications, coarse popcorn-like calcifications, large rod-like calcifications, round calcifications, lucent-centered calcifications, eggshell or rim calcifications, milk of calcium calcifications, suture calcification and dystrophic calcifications. Malignancy suspicious calcifications are amorphous and coarse heterogeneous calcifications. Malignancy highly suspicious calcifications are fine pleomorphic, Fine-linear and fine linear-branching calcifications.


One of the main characteristics to consider in the detection of calcifications is that they are generally very small. Their size varies from 0.1 mm to 1 mm and the average diameter is 0.3 mm. Small calcifications may be missed due to the overlapping breast parenchyma. Another issue is that in regions where the background tissue is dense, it is very difficult to localize the calcifications. Finally,

Calcifications sometimes have a low contrast to the background and can be mistaken as noise in the inhomogeneous background.


Although architectural distortions are less prevalent than masses or calcifications, they are the third most common mammographic sign of cancer and are strongly suggestive of malignancy Architectural distortion is defined as distortion of the normal architecture with no definite mass visible, including speculations radiating from a point and focal retraction or distortion at the edge of the parenchyma. Architectural distortion of breast tissue can indicate malignant changes especially when integrated with visible lesions such as mass, asymmetry or calcifications. Architectural distortion can be classified as benign when including scar and Soft-tissue damage due to trauma.


Methods for detection of architectural distortion are often included in mass detection algorithms. However, methods designed exclusively for the detection of architectural distortion can achieve better performance than the application of methods for the detection of speculated masses, which may rely on the presence of a central mass. In order to detect architectural distortion some methods are based on the detection of speculated lesions, on the detection of architectural distortion around the skin line and within the mammary gland and some are texture-based. Accurate detection of architectural distortion could be the key to efficient detection of early breast cancer, at pre-mass formation stages.


The use of computers in processing and analyzing biomedical images allows more accurate diagnose by a radiologist. Humans are susceptible to committing errors and their analysis is usually subjective and qualitative. Objective and quantitative analysis facilitated by the application of computers to biomedical image analysis leads to a more accurate diagnostic decision by the physician. Computer-aided detection (CADe) is designed to provide the radiologist with visual prompts on Series of mammograms. It works by marking a mammogram with marks that indicate regions where the detection algorithm recognizes a suspicious entity that warrants further investigation, thereby complementing the radiologists' interpretation. Findings in a number of studies have demonstrated that CADe has the ability to detect and prompt mammographic signs of cancer with the potential to increase cancer detection rates by approximately 20% .

If a patient's medical history and radiologist's findings are taken into account, together with computer-aided detection data that provides diagnostic output, a computer-aided diagnosis (CADx) system exists. Sometimes, both computer-aided detection and computer-aided diagnosis are referred to as CAD.

In most developed CADe and CADx programs, there are some common steps that have to be fulfilled in order to find the suspect lesions. Most detection algorithms consist of two stages. In stage 1, the aim is to detect suspicious lesions at a high sensitivity. In stage 2, the aim is to reduce the number of false positives without decreasing the sensitivity drastically. In some approaches some of the steps may involve very simple methods or be skipped entirely. Most diagnosis algorithms (CADx) begin with a region of interest (ROI) containing the abnormality. The output of a CADx system may be the likelihood of malignancy or a management recommendation. Different research groups have worked on different components of the problem and human interaction may occur at various stages. For example, many CADx algorithms start with manually segmented ROIs.

In the preprocessing step the breast is segmented in order to limit the search for abnormalities without undue influence from the background of the mammogram and some filtering or normalization is accomplished in order to improve the quality of the image and reduce the noise. The next step, feature extraction is one of the most important factors that affect the CAD performance. Basically, researchers Have investigated two types of features: those traditionally used by radiologists (gradient-based, intensity-based and geometric features) and high order features that may not be as intuitive to radiologists (e.g. texture features). Critical issue in CAD design is the choice of the best set of features for detecting or classifying the suspect lesions. The whole set of features may include redundant or irrelevant information. One feature taken alone might not be significant for classification but might be very significant if combined with other features. In order to decide which features are best suited for classification, feature selection is used. Feature selection is defined as selecting a smaller feature

Subset of size m from a set of d features that leads to the largest value of some classifier performance function. Finally, a classification (false-positive reduction) step is preformed, where on the basis of the mentioned features false signals are separated from the suspect lesions by means of a classifier. In the other words, the candidate lesions are first located and then further analyzed in a feature analysis and classification phase to determine the final classification of each candidate.


The Results are done by using MATLAB Software. Figure 5: shows that Mammography Image Enhancement and detection of Breast Cancer Cell. Figure 6: shows that each stage of Cancer Diagnosis Image

Figure 5: Mammography Image Enhancement and detection of Breast Cancer Cell

Figure 6: Cancer Diagnosis Image


Thus to participate in the medical world using the electronics we did an effective project for wireless transmission of medical image by 2D telemetry and to focus on the cancer disease. We processed the tumor identification by MATLAB programming which helps the doctor for diagnosis of this disease. By our project we think we are serving to society